Organization spend limits and email alerts

You can now set a monthly spending limit for your organization from the Billing page in the Neon Console. When spend reaches 80% and 100% of that limit, Neon sends email alerts to organization admins. Spend is evaluated about every 15 minutes, and reminder emails continue weekly until you raise the limit or the billing cycle resets.

When you enable a limit, you enter a dollar amount and choose threshold behavior. Email alerts are available now. In a future release, you'll be able to suspend computes automatically when the limit is reached.

To get started, see Spending limits.

Monitor snapshot storage consumption

Building on the recent snapshot size fields added to snapshot API responses, the Retrieve project consumption metrics endpoint now supports a snapshot_storage_bytes_month metrics parameter. Use it to track monthly snapshot storage in your project-level consumption reporting.

Example response body (excerpt):

{
  "timeframe_start": "2026-02-05T00:00:00Z",
  "timeframe_end": "2026-02-06T00:00:00Z",
  "metrics": [
    { "metric_name": "root_branch_bytes_month", "value": 758611968 },
    { "metric_name": "instant_restore_bytes_month", "value": 983488 },
    { "metric_name": "snapshot_storage_bytes_month", "value": 0 }
  ]
}

For the full list of metrics, see Querying consumption metrics.

One-click Neon MCP setup for Kiro

The Neon MCP Server now supports an Add to Kiro badge for one-click MCP setup. Thanks to Anil Maktala for the contribution.

Build durable agents with Pydantic AI, DBOS, and Neon

Multi-step agents depend on LLMs and tools that can time out, rate limit, or fail mid-run. A practical pattern is durable execution: checkpoint progress in Postgres so a workflow can resume after a crash or retry without redoing expensive steps from scratch. DBOS provides that durable execution layer, Pydantic AI structures agent logic and tool orchestration, and Neon backs the database so checkpoint state lives in serverless Postgres that scales with your workload.

For a full walkthrough with a working example, see Building Durable AI Agents with Pydantic AI, DBOS, and Neon.

Making Neon docs work for AI agents

We've been optimizing our docs for AI agents. The post Agents grew up, so did our docs covers what worked, what didn't, and what we're still figuring out. It includes findings from a scan of 250+ doc sites, plus details like our MDX-to-Markdown pipeline, content negotiation (serving Markdown to agents or by appending .md to any doc URL), agent-aware 404 handling, and a restructured llms.txt index.